Possibilistic linear systems and their application to the linear regression model
Fuzzy Sets and Systems
On assessing the H value in fuzzy linear regression
Fuzzy Sets and Systems
Computers and Industrial Engineering
Fuzzy logic and optimization models for implementing QFD
Proceedings of the 23rd international conference on on Computers and industrial engineering
A linear regression model using triangular fuzzy number coefficients
Fuzzy Sets and Systems
Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks
Fuzzy Sets and Systems
A new approach to quality function deployment planning with financial consideration
Computers and Operations Research
Fuzzy least-squares algorithms for interactive fuzzy linear regression models
Fuzzy Sets and Systems - Theme: Modeling and learning
Fuzzy regression by fuzzy number neural networks
Fuzzy Sets and Systems
A methodology of determining aggregated importance of engineering characteristics in QFD
Computers and Industrial Engineering
Computers and Industrial Engineering
Quality function deployment: a comprehensive literature review
International Journal of Data Analysis Techniques and Strategies
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A grey method of prioritizing engineering characteristics in QFD
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A rough set approach for estimating correlation measures in quality function deployment
Information Sciences: an International Journal
Rough set-based approach for modeling relationship measures in product planning
Information Sciences: an International Journal
Affective and cognitive design for mass personalization: status and prospect
Journal of Intelligent Manufacturing
Use of ANP weighted crisp and fuzzy QFD for product development
Expert Systems with Applications: An International Journal
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Product planning is one of four important processes in new product development (NPD) using quality function deployment (QFD), which is a widely used customer-driven approach. In our opinion, the first problem to be solved is how to incorporate both qualitative and quantitative information regarding relationships between customer requirements (CRs) and engineering characteristics (ECs) as well as those among ECs into the problem formulation. Owing to the typical vagueness or imprecision of functional relationships in a product, product planning is becoming more difficult, particularly in a fuzzy environment. In this paper, an asymmetric fuzzy linear regression approach is proposed to estimate the functional relationships for product planning based on QFD. Firstly, by integrating the least-squares regression into fuzzy linear regression, a pair of hybrid linear programming models with asymmetric triangular fuzzy coefficients are developed to estimate the functional relationships for product planning under uncertainties. Secondly, using the basic concept of fuzzy regression, asymmetric triangular fuzzy coefficients are extended to asymmetric trapezoidal fuzzy coefficients, and another pair of hybrid linear programming models with asymmetric trapezoidal fuzzy coefficients is proposed. The main advantage of these hybrid-programming models is to integrate both the property of central tendency in least squares and the possibilistic property in fuzzy regression. Next, the illustrated example shows that trapezoidal fuzzy number coefficients have more flexibility to handle a wider variety of systematic uncertainties and ambiguities that cannot be modeled efficiently using triangular number fuzzy coefficients. Both asymmetric triangular and trapezoidal fuzzy number coefficients can be applicable to a much wider variety of design problems where uncertain, qualitative, and fuzzy relationships are involved than when symmetric triangular fuzzy numbers are used. Finally, future research direction is also discussed.